Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior
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Abstract
Non-recurrent deep convolutional neural networks (CNNs) are currently the best at modeling core object recognition, a behavior that is supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. If recurrence is critical to this behavior, then primates should outperform feedforward-only deep CNNs for images that require additional recurrent processing beyond the feedforward IT response. Here we first used behavioral methods to discover hundreds of these 'challenge' images. Second, using large-scale electrophysiology, we observed that behaviorally sufficient object identity solutions emerged ~30 ms later in the IT cortex for challenge images compared with primate performance-matched 'control' images. Third, these behaviorally critical late-phase IT response patterns were poorly predicted by feedforward deep CNN activations. Notably, very-deep CNNs and shallower recurrent CNNs better predicted these late IT responses, suggesting that there is a functional equivalence between additional nonlinear transformations and recurrence. Beyond arguing that recurrent circuits are critical for rapid object identification, our results provide strong constraints for future recurrent model development.
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Analogous approaches have proven successful in relating the structure of neural activity to computational functions in other domains, including vision, audition, olfaction, thermosensation, perceptual discrimination, facial perception, and navigation.
“Analogous approaches have proven successful in relating the structure of neural activity to computational functions in other domains, including vision 17–19, audition 20, olfaction 21,22, thermosensation 23, perceptual discrimination 24, facial perception 25, and navigation 26,27.”
From A virtual rodent predicts the structure of neural activity across behaviours (2024) · cites this paper as [18]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783 · see citing works
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636 · see citing works
Speed of processing in the human visual system
Thorpe, S., Fize, D., & Marlot, C. (1996) · Nature · doi:10.1038/381520a0 · Cited by 3,538 · see citing works
Preface: Cerebral Cortex Has Come of Age
Goldman, P. S., & RAKIC, P. T. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1-a · Cited by 3,412 · see citing works
Return of the Devil in the Details: Delving Deep into Convolutional Nets
Chatfield, K., Simonyan, K., Vedaldi, A., & Zisserman, A. (2014) · Proceedings of the British Machine Vision Conference 2014 · doi:10.5244/c.28.6 · Cited by 3,335 · see citing works
The distinct modes of vision offered by feedforward and recurrent processing
Lamme, V. A. F., & Roelfsema, P. R. (2000) · Trends in Neurosciences · doi:10.1016/s0166-2236(00)01657-x · Cited by 2,338 · see citing works
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924 · see citing works
Self-citationHow does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790 · see citing works
Self-citationTop-down facilitation of visual recognition
Bar, M., Kassam, K. S., Ghuman, A. S., Boshyan, J., Schmid, A. M., Dale, A. M., Hämäläinen, M. S., Marinkovic, K., Schacter, D. L., Rosen, B. R., et al. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0507062103 · Cited by 1,489 · see citing works
Controlling low-level image properties: The SHINE toolbox
Willenbockel, V., Sadr, J., Fiset, D., Horne, G. O., Gosselin, F., & Tanaka, J. W. (2010) · Behavior Research Methods · doi:10.3758/brm.42.3.671 · Cited by 1,166 · see citing works
The "Wake-Sleep" Algorithm for Unsupervised Neural Networks
Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. M. (1995) · Science · doi:10.1126/science.7761831 · Cited by 1,070 · see citing works
Guclu, U., & van Gerven, M. A. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5023-14.2015 · Cited by 961 · see citing works
van Kerkoerle, T., Self, M. W., Dagnino, B., Gariel-Mathis, M.-A., Poort, J., van der Togt, C., & Roelfsema, P. R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1402773111 · Cited by 918 · see citing works
Fast Readout of Object Identity from Macaque Inferior Temporal Cortex
Hung, C. P., Kreiman, G., Poggio, T., & DiCarlo, J. J. (2005) · Science · doi:10.1126/science.1117593 · Cited by 854 · see citing works
Self-citationCadieu, C. F., Hong, H., Yamins, D. L. K., Pinto, N., Ardila, D., Solomon, E. A., Majaj, N. J., & DiCarlo, J. J. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003963 · Cited by 824 · see citing works
Self-citationRosenholtz, R. E., Li, Y., & Nakano, L. (2007) · Journal of Vision · doi:10.1167/7.2.17 · Cited by 805 · see citing works
Return of the Devil in the Details: Delving Deep into Convolutional NetsPreprint
Chatfield, K., Simonyan, K., Vedaldi, A., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1405.3531 · Cited by 663 · see citing works
Riesenhuber, M., & Poggio, T. (2000) · Nature Neuroscience · doi:10.1038/81479 · Cited by 658 · see citing works
Top-down signal from prefrontal cortex in executive control of memory retrieval
Tomita, H., Ohbayashi, M., Nakahara, K., Hasegawa, I., & Miyashita, Y. (1999) · Nature · doi:10.1038/44372 · Cited by 615 · see citing works
Why is Real-World Visual Object Recognition Hard?
Pinto, N., Cox, D., & DiCarlo, J. J. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0040027 · Cited by 537 · see citing works
Self-citationBrain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?Preprint
Schrimpf, M., Kubilius, J., Hong, H., Majaj, N. J., Rajalingham, R., Issa, E. B., Kar, K., Bashivan, P., Prescott-Roy, J., Geiger, F., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/407007 · Cited by 463 · see citing works
Self-citationA face feature space in the macaque temporal lobe
Freiwald, W. A., Tsao, D. Y., & Livingstone, M. S. (2009) · Nature Neuroscience · doi:10.1038/nn.2363 · Cited by 441 · see citing works
Dynamic Population Coding of Category Information in Inferior Temporal and Prefrontal Cortex
Meyers, E. M., Freedman, D. J., Kreiman, G., Miller, E. K., & Poggio, T. (2008) · Journal of Neurophysiology · doi:10.1152/jn.90248.2008 · Cited by 408 · see citing works
Liu, H., Agam, Y., Madsen, J. R., & Kreiman, G. (2009) · Neuron · doi:10.1016/j.neuron.2009.02.025 · Cited by 388 · see citing works
Evaluation of autofocus functions in molecular cytogenetic analysis
Santos, A., SOLÓRZANO, C. O. D., Vaquero, J. J., et al. (1997) · Journal of Microscopy · doi:10.1046/j.1365-2818.1997.2630819.x · Cited by 380 · see citing works
Rajalingham, R., Issa, E. B., Bashivan, P., Kar, K., Schmidt, K., & DiCarlo, J. J. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.0388-18.2018 · Cited by 360 · see citing works
Self-citationMasking Disrupts Reentrant Processing in Human Visual Cortex
Fahrenfort, J. J., Scholte, H. S., & Lamme, V. A. F. (2007) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2007.19.9.1488 · Cited by 344 · see citing works
A backward progression of attentional effects in the ventral stream
Buffalo, E. A., Fries, P., Landman, R., Liang, H., & Desimone, R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907658106 · Cited by 306 · see citing works
Impact of learning on representation of parts and wholes in monkey inferotemporal cortex
Baker, C. I., Behrmann, M., & Olson, C. R. (2002) · Nature Neuroscience · doi:10.1038/nn960 · Cited by 293 · see citing works
Masking Interrupts Figure-Ground Signals in V1
Lamme, V. A. F., Zipser, K., & Spekreijse, H. (2002) · Journal of Cognitive Neuroscience · doi:10.1162/089892902320474490 · Cited by 285 · see citing works
A source for feature-based attention in the prefrontal cortex
Bichot, N. P., Heard, M. T., DeGennaro, E. M., & Desimone, R. (2015) · Neuron · doi:10.1016/j.neuron.2015.10.001 · Cited by 283 · see citing works
Seger, C. A. (2007) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2007.07.010 · Cited by 272 · see citing works
Comparing deep neural networks against humans: object recognition when the signal gets weakerPreprint
Geirhos, R., Janssen, D., Schütt, H. H., Rauber, J., Bethge, M., & Wichmann, F. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06969 · Cited by 224 · see citing works
Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition
Spoerer, C. J., McClure, P., & Kriegeskorte, N. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01551 · Cited by 219 · see citing works
Recurrent computations for visual pattern completion
Tang, H., Schrimpf, M., Lotter, W. E., Moerman, C., Paredes, A. L., Caro, J. O., Hardesty, W., Cox, D., & Kreiman, G. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719397115 · Cited by 216 · see citing works
Rockland, K. S., & Virga, A. (1989) · The Journal of Comparative Neurology · doi:10.1002/cne.902850106 · Cited by 215 · see citing works
Selective visual attention enables learning and recognition of multiple objects in cluttered scenes
Walther, D. B., Rutishauser, U., Christof Koch, A., & Perona, P. (2005) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2004.09.004 · Cited by 199 · see citing works
Majaj, N. J., Hong, H., Solomon, E. A., & DiCarlo, J. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.5181-14.2015 · Cited by 193 · see citing works
Self-citationDirect Temporal-Occipital Feedback Connections to Striate Cortex (V1) in the Macaque Monkey
Rockland, K. S., & Van Hoesen, G. W. (1994) · Cerebral Cortex · doi:10.1093/cercor/4.3.300 · Cited by 191 · see citing works
Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual CortexPreprint
Liao, Q., & Poggio, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1604.03640 · Cited by 177 · see citing works
CORnet: Modeling the Neural Mechanisms of Core Object RecognitionPreprint
Kubilius, J., Schrimpf, M., Nayebi, A., Bear, D. M., Yamins, D. L. K., & DiCarlo, J. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/408385 · Cited by 176 · see citing works
Self-citationMostafapour, S. P., Cochran, S. L., del Puerto, N. M., & Rubel, E. W. (2000) · The Journal of Comparative Neurology · doi:10.1002/1096-9861(20001030)426:4 · Cited by 156 · see citing works
Divergent feedback connections from areas V4 and TEO in the macaque
Rockland, K. S., Saleem, K. S., & Tanaka, K. (1994) · Visual Neuroscience · doi:10.1017/s0952523800002480 · Cited by 154 · see citing works
Rolls, E. T., Baylis, G. C., & Leonard, C. M. (1985) · Vision Research · doi:10.1016/0042-6989(85)90091-4 · Cited by 133 · see citing works
Stojanoski, B. B., & Cusack, R. (2014) · Journal of Vision · doi:10.1167/14.12.6 · Cited by 117 · see citing works
Task Dependence of Visual and Category Representations in Prefrontal and Inferior Temporal Cortices
McKee, J. L., Riesenhuber, M., Miller, E., & Freedman, D. J. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1660-14.2014 · Cited by 110 · see citing works
Comparison of Object Recognition Behavior in Human and Monkey
Rajalingham, R., Schmidt, K., & DiCarlo, J. J. (2015) · Journal of Neuroscience · doi:10.1523/jneurosci.0573-15.2015 · Cited by 105 · see citing works
Self-citationNeuronal Processing: How fast is the speed of thought?
Tovée, M. J. (1994) · Current Biology · doi:10.1016/s0960-9822(00)00253-0 · Cited by 104 · see citing works
Neural representation for object recognition in inferotemporal cortex
Lehky, S. R., & Tanaka, K. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2015.12.001 · Cited by 89 · see citing works
Task alters category representations in prefrontal but not high-level visual cortex
Bugatus, L., Weiner, K. S., & Kalanit Grill-spector, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.062 · Cited by 69 · see citing works
Dynamic representation of partially occluded objects in primate prefrontal and visual cortex
Fyall, A. M., El-Shamayleh, Y., Choi, H., Shea‐Brown, E., & Pasupathy, A. (2017) · eLife · doi:10.7554/elife.25784 · Cited by 57 · see citing works
Eight open questions in the computational modeling of higher sensory cortex
Yamins, D. L. K., & DiCarlo, J. J. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.02.001 · Cited by 44 · see citing works
Self-citationAdversarial Examples that Fool both Human and Computer Vision
Elsayed, G. F., Shankar, S., Cheung, B., Papernot, N., Kurakin, A., Goodfellow, I., & Sohl‐Dickstein, J. (2018) · Cited by 39 · see citing works
Serial grouping of 2D-image regions with object-based attention in humans
Jeurissen, D., Self, M. W., & Roelfsema, P. R. (2016) · eLife · doi:10.7554/elife.14320 · Cited by 38 · see citing works
Contrast induced changes in response latency depend on stimulus specificity
Oram, M. W. (2009) · Journal of Physiology-Paris · doi:10.1016/j.jphysparis.2009.11.021 · Cited by 32 · see citing works
Face adaptation does not improve perceptual salience
Ng, M., Boynton, G. M., & Fine, I. (2010) · Journal of Vision · doi:10.1167/7.9.11 · Cited by 14 · see citing works
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- Authors
- Kar, K., Kubilius, J., Schmidt, K., Issa, E. B., & DiCarlo, J. J.
- Affiliations
- McGovern Institute for Brain Research (US)
- Corresponding author
- Kar, Kohitij
- Venue
- Nature Neuroscience
- Published
- 2019-04-29
- Volume
- 22
- Issue
- 6
- Pages
- 974-983
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- PMC
- PMC8785116
- OpenAlex
- W2949512190
- Semantic Scholar
- 749118e990a532dddf9f573676e9d178f8442e2d
- Cited by
- 496
- References
- 63
- Citations by year
- 2026: 54 · 2025: 58 · 2024: 55 · 2023: 72 · 2022: 86 · 2021: 77 · 2020: 58 · 2019: 33 · 2018: 3
- FWCI
- 36.92
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- Funders
- National Science Foundation (1231216)
- European Commission (705498, HORIZON2020)
- Companhia Brasileira de Metalurgia e Mineração (CCF-1231216)
- Multidisciplinary University Research Initiative
- Office of Naval Research (MURI-114407)
- National Eye Institute (K99EY022671, R00EY022671, R01EY014970)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- recurrent circuits, ventral stream, object recognition, inferior temporal cortex, recurrent convolutional neural network, primate visual cortex
- MeSH terms
- Animals, Humans, Macaca mulatta, Temporal Lobe, Visual Perception, Neural Networks, Computer, Recognition, Psychology
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